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This paper proposes a novel fragile watermarking scheme for digital image authentication which is based on Singular Value Decomposition(SVD) and grouped blocks. The watermark bits which include two types of bits are inserted into the least significant bit(LSB) plane of the host image using the adaptive chaotic map to determine the positions. The groped blocks break the block-wise independence and therefore can withstand the Vector Quantization attack(VQ attack). The inserting positions are related to the statistical information of image block data, in order to increase the security and provide an auxiliary way to authenticate the image data. The effectiveness of the proposed scheme is checked by a variety of attacks, and the experimental results prove that it has a remarkable tamper detection ability and also has a precise locating ability.

Background: This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data.Methods: This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals; all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed. In all, 73 texture features were extracted from manually delineated lesion volumes, and 23 three-dimensional (3D) wavelets with eight decomposition modes were implemented to compare and validate the value of wavelet transformation for grade assessment. Finally, the optimal machine learning pipeline, valuable radiomic features, and final radiomic models were determined. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve were used to determine the diagnostic performance and clinical utility of the models.Results: Of the 187 lesions, 108 (57.75%) were diagnosed as mild lesions and 79 (42.25%) as moderate/ severe lesions. All selected radiomic features showed significant correlations with the grade of COVID-19 pulmonary lesions (P<0.05). Biorthogonal 1.1 (bior1.1) LLL was determined as the optimal wavelet transform mode. The wavelet transforming radiomic model had an AUC of 0.910 in the test cohort, outperforming the original radiomic model (AUC =0.880; P<0.05). Decision analysis showed the radiomic model could add a net benefit at any given threshold of probability.Conclusions: Wavelet transformation can enhance CT texture features. Wavelet transforming radiomics based on CT images can be used to effectively assess the grade of pulmonary lesions caused by COVID-19, ^ ORCID: Zekun Jiang,

Purpose This study investigates the efficiency of deep learning models in the automated diagnosis of Hashimoto’s thyroiditis (HT) using real-world ultrasound data from ultrasound examinations by computer-assisted diagnosis (CAD) with artificial intelligence. Methods We retrospectively collected ultrasound images from patients with and without HT from two hospitals in China between September 2008 and February 2018. Images were divided into a training set (80%) and a validation set (20%). We ensembled nine convolutional neural networks (CNNs) as the final model (CAD-HT) for HT classification. The model’s diagnostic performance was validated and compared from two hospital validation sets. We also compared the accuracy of CAD-HT against seniors/junior radiologists. Subgroup analysis of CAD-HT performance in different thyroid hormone levels, such as hyperthyroidism, hypothyroidism, and euthyroidism, was also evaluated. Results 39280 ultrasound images from 21118 patients were included in this study. The accuracy, sensitivity, specificity of the ensemble HT-CAD model were 0.892, 0.890 and 0.895, respectively. HT-CAD performance between two hospitals was not significantly different. The HT-CAD model achieved a higher performance (P &lt; 0.001) when compared to senior radiologists, with a nearly 9% accuracy improvement. HT-CAD had almost similar accuracy (from 0.871 to 0.894) among the three differences of thyroid hormone level subgroups. Conclusion The HT-CAD strategy based on CNN significantly improved the radiologists’ diagnostic accuracy on HT. Our model demonstrates good performance and robustness in different hospitals and for different thyroid hormone levels.

Abstract-This paper proposes a robust watermarking approach based on Discrete Cosine Transform (DCT) domain that combines Quick Response (QR) Code and chaotic system. When embed the watermark, the high error correction performance and the strong decoding capability of QR Code are utilized to decode the text watermark information which improves the robustness of the watermarking algorithm. Then the QR Code image is encrypted with chaotic system to enhance the security of this approach. Finally the encrypted image is embedded to the carrier image's DCT blocks after they underwent block-based Arnold scrambling transformation. During the extraction process, as long as the QR Code image can be decoded, the completeness and accuracy of the text watermarking information can be guaranteed. The results of simulation experiment show that this approach has high robustness and security and has, therefore, some practical value in the copyright protection.